Electronic Support Measures (ESM) are essential in tactical military operations. For example, airborne defense employing high speed tactical fighters requires fast and accurate target data, including bearing, range and heading, in an environment that typically includes multiple targets and clutter. Although present ESM data systems can provide fairly accurate input, the intermittent nature of the data causes significant track switching problems for closely spaced tracks. FIG. 1 illustrates this in the Area Defense context.
FIG. 2 shows a representative prior art airborne tracking system (tracker 10). A selected spatial region is continually scanned by system sensors that via a sensor processor convert the emitter signals to emitter measurements that are input to an information processor. The information processor is programmed to convert the data to kinematic equations, that is, into a form that quantifies a target emitter's position, velocity, and acceleration. An exemplary kinematic equation for predicting incoming measurements emitted by a target in a known state is:xk+1=Akxk+wk;wk  Eq. Awhere xk is a vector containing quantities of interest such as a target's position, velocity, and acceleration. Kinematic behavior in Eq. A is described by the transition matrix, Ak, and the plant noise term, wk, which represents the kinematic uncertainty in the model. Incoming measurements emitted by a target can be predicted according to:zk=Ckxk+vk;vk  Eq. Bwhere zk are measurements and vk (sensor uncertainty index) captures the sensor uncertainty. Any typical tracking system employs these two equations. State measurements are generated for targets by iteratively filtering the target state and predicting the position of the next measurement, and comparing the estimated measurements with the actual measurements at the next scan. A deficiency with this approach is that the trajectory model is assumed known and constant. For example, when tracking radar reports, a second order trajectory model is typically used. However, if the target exhibits characteristics which are not consistent with the assumed second order model, either the target prediction will exhibit additional noise or the track will exhibit significant bias.
The simple Kalman filter is a typical such system, which assumes that the plant order associated with the trajectory is well known and constant. Although this assumption is valid for a 2 state trajectories with a consistently high data rate, i.e., everything looks approximately like a straight line if the data rate is high enough, the assumption is not valid for the Area Defense problem, illustrated in FIG. 1, due to it's intermittent nature.
Tracking involves receiving emitter signals from an airborne target and processing the signals in order to estimate the target's speed and position. A received signal includes noise that interferes with the processing and interpretation of the primary emitter signal and thereby corrupts the data. The problem is exacerbated when there are multiple emitters or targets, targets engaging in evasive maneuvers, or false or misleading detections.
Another approach described in U.S. Pat. No. 5,842,156, issued Nov. 24, 1998, Hong et al., is a target tracking system using multiresolution and multirate techniques. Data obtained from a target scanning region is reduced spatially and temporally to reduce the amount of data for processing. A deficiency with this approach is that it addresses this problem by assigning multiple data rates to filters associated with an Interactive Multiple Model (IMM) system to compensate for the possible lags due to the use of low order models in the IMM Kalman filter bank. This is commonly used approximation which attempts to linearize a nonlinear trajectory model.
It is therefore desirable to provide a tracking system with the added capability of identifying an emitter target and accurately providing its bearing, elevation, and ESM data. Such a tracking system provides not only the basis for prediction, but also the ESM control algorithm since ESM trajectory (or equivalently interferometer phase angle) and ESM parameters may be fed back to the front-end ESM system to provided adaptive gating in the target parameter space as well as aid the deinterleaving process.